Despite the success of the existing methods [1]}, [2]}, [3]},
they often model the OOD detection problem as a binary classification task and take OOD data as the positive class, which indicates that OOD data is a necessary part in the training phase.
In this sense, the proposed OOD detectors actually perform in a discriminative manner [4]}.
However, the discriminative model for OOD detection can easily be biased to the training OOD dataset, which causes that the other OOD datasets are still mysteries to the model.
Contrary to the discriminative model,
the generative model is intrinsically able to measure the predictive uncertainty and detect the outliers for the given DNN model.
The rationale is that the generative model takes OOD detection as a one-class classification task, which is consistent with the general knowledge in classic machine learning [5]}.
However, outlier detection in classic machine learning is often model-independent due to poor feature extraction for the (statistical) machine learning models.
In the era of deep learning, latent features extracted from DNN models could be effective in constructing a generative OOD detector.
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